aiqbench analysis
The article is a practical guide to open-weight versus closed-weight models, using OpenRouter usage, pricing and benchmark data. Its headline claims are that open-weight models now carry most of the top 50 models' tokens, cost roughly a quarter as much per token, and still trail the best closed models by about 11 points on the Artificial Analysis Intelligence Index. For readers, the key points are the licensing terms, which vary widely, and the trade-off between cost and quality, which should be tested on their own workloads rather than taken from aggregate figures.
Open-Weight vs Closed-Weight Models: What's the Difference?
An open-weight model publishes its trained weights, so anyone can run, fine-tune or serve it. A closed-weight model is only available from its maker. We used a year of OpenRouter usage data, our price catalog and benchmark scores to show what that difference changes for cost, providers and quality.
An open-weight model is one whose trained weights are published, so anyone can download it, run it on their own hardware, fine-tune it, or serve it to others. A closed-weight model keeps its weights private, so the only way to use it is through the company that built it or a cloud it licenses to. Across OpenRouter, open-weight models went from 27% of the top 50 models’ tokens in October 2025 to 72% in late September 2026. Open-weight models publish their weights, so anyone can run, fine-tune or serve them. Closed-weight models are only available from their maker and the clouds it licenses. Open-weight models handled 72% of the top 50 models’ tokens on OpenRouter in late September 2026, up from 27% a year earlier. They cost about a quarter as much per token, mostly because many providers compete to serve the same model. The best closed-weight models still score about 11 points higher on the Artificial Analysis Intelligence Index. Start on an open-weight model, test it, and use a closed-weight model for…
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